PROJECT SUMMARY Knowledge from the recent clinical trials suggests that over 80% of head and neck cancer (HNC) are hypo-immunogenic cold tumors and non-responsive to immune checkpoint receptors (ICR) blockade. With the emerging combinatorial strategies for cold cancer, precise identification of this group of tumors is essential for the selection of optimal treatment protocols. However, there is no consistent algorithm available to assess the global immune profile of HNC. Most of the current immunoscore methods are based on immunohistochemical (IHC) staining of a limited panel of biomarkers, which prevents a precise annotation of the landscape of tumor- infiltrating lymphocytes (TIL). The IHC method is technically sensitive, and may present inter-institutional and inter-pathologists variations. Moreover, the current immunoscore only emphasizes on a few T-cell subsets, and does not integrate cancer genomic features that modulate tumor response to immune killing. In fact, strong evidence suggests that the type I interferon (IFN-I) pathway plays a fundamental role in HNC response to effector immune cells. Thus, leveraging global TIL profiles and cancer genomic features offers an unprecedented opportunity to classify HNC based on its immunogenicity. The current robust methods for cellular deconvolution are sensitive to outliers, which are frequently observed in the whole tumor RNA-Seq datasets. Our recent studies show that a novel machine learning tool Fast And Robust DEconcolution of Expression Profiles (FARDEEP), which adaptively detects and removes outliers, exhibits superior accuracy in immune cell deconvolution. In precise alignment with the FOA, the overarching hypothesis of this project is that a compound immunoscore integrating FARDEEP-assisted TIL deconvolution and cancer genomics can effectively identify cold HNC. To achieve this goal, our two immediate next steps are: **(1) We will develop a robust model-free approach to identify TIL-driving oncogenic pathways; **(2) We will construct a compound immunoscore integrating cancer genomic features and TIL profiles to identify cold HNC. These studies will develop a novel ?statistical methodology appropriate for analyzing genome-wide data? and provide ?statistical analysis of existing genome-wide data? for an NIDCR priority disease. This project will refine a robust and novel immune-cell deconvolution machine learning tool and characterize central oncogenic pathways that shift the TIL landscape. The new immunogenomics algorithms will streamline the immunoscoring method to effectively stratify HNC and contribute to the precision selection of combinatorial treatments.